// src/services/semantic_conversion.rs

use crate::interfaces::{
    SemanticConverter, ConversionMode, QualityLevel, Result,
    QualityFeedback, ConversionStatistics, QualityMetrics,
    ConverterConfiguration, PerformanceSettings, CacheSettings,
    LoggingSettings, LogLevel, CacheStrategy
};
use crate::models::{
    ConversionRequest, ConversionResult, TransformationRules,
    OPCUAModel, DataSourceSchema, QualityMetrics as ModelQualityMetrics,
    NodeId, DataType, OPCUANode, OPCUAObjectType, OPCUAVariable,
    ReferenceType, NamespaceArray
};
use std::collections::{HashMap, HashSet};
use std::time::{Duration, Instant};
use tokio::sync::RwLock;
use serde::{Deserialize, Serialize};
use async_trait::async_trait;
use anyhow::{anyhow, Result as AnyhowResult};
use log::{debug, info, warn, error};

/// 工业语义转换器实现
/// 支持华为云物联网平台到OPC UA的语义转换
pub struct IndustrialSemanticConverter {
    /// 转换器配置
    config: RwLock<ConverterConfiguration>,
    /// 转换统计
    statistics: RwLock<ConversionStatistics>,
    /// 规则引擎
    rule_engine: RuleEngine,
    /// 语义缓存
    semantic_cache: SemanticCache,
    /// 学习引擎
    learning_engine: LearningEngine,
    /// 质量评估器
    quality_assessor: QualityAssessor,
}

/// 规则引擎 - 处理转换规则和语义映射
pub struct RuleEngine {
    /// 转换规则集合
    transformation_rules: HashMap<String, TransformationRule>,
    /// 语义映射字典
    semantic_mappings: SemanticMappings,
    /// 行业特定规则
    industry_rules: IndustrySpecificRules,
}

/// 语义映射字典
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SemanticMappings {
    /// 数据类型映射
    data_type_mappings: HashMap<String, String>,
    /// 单位映射
    unit_mappings: HashMap<String, String>,
    /// 语义标签映射
    semantic_tag_mappings: HashMap<String, Vec<String>>,
    /// 命名空间映射
    namespace_mappings: HashMap<String, String>,
}

/// 行业特定规则
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IndustrySpecificRules {
    /// 制造业规则
    manufacturing: ManufacturingRules,
    /// 机器人应用规则
    robotics: RoboticsRules,
    /// 供应链管理规则
    supply_chain: SupplyChainRules,
    /// 交通工程规则
    traffic_engineering: TrafficEngineeringRules,
}

/// 制造业规则
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ManufacturingRules {
    /// 设备类型映射
    equipment_types: HashMap<String, String>,
    /// 工序映射
    process_mappings: HashMap<String, String>,
    /// 质量标准映射
    quality_standards: HashMap<String, String>,
}

/// 机器人应用规则
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RoboticsRules {
    /// 关节类型映射
    joint_types: HashMap<String, String>,
    /// 运动类型映射
    motion_types: HashMap<String, String>,
    /// 控制模式映射
    control_modes: HashMap<String, String>,
}

/// 供应链管理规则
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SupplyChainRules {
    /// 物流状态映射
    logistics_status: HashMap<String, String>,
    /// 库存类型映射
    inventory_types: HashMap<String, String>,
    /// 供应商等级映射
    supplier_levels: HashMap<String, String>,
}

/// 交通工程规则
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrafficEngineeringRules {
    /// 交通信号映射
    traffic_signals: HashMap<String, String>,
    /// 车辆类型映射
    vehicle_types: HashMap<String, String>,
    /// 道路状态映射
    road_conditions: HashMap<String, String>,
}

/// 语义缓存
pub struct SemanticCache {
    /// 缓存存储
    cache_storage: RwLock<HashMap<String, CachedSemanticResult>>,
    /// 缓存配置
    config: CacheSettings,
}

/// 缓存的语义转换结果
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CachedSemanticResult {
    /// 转换结果
    result: ConversionResult,
    /// 缓存时间
    cached_at: Instant,
    /// 访问次数
    access_count: u64,
    /// 质量评分
    quality_score: f64,
}

/// 学习引擎 - 基于反馈优化转换规则
pub struct LearningEngine {
    /// 学习算法类型
    algorithm_type: LearningAlgorithm,
    /// 训练数据
    training_data: Vec<TrainingExample>,
    /// 模型参数
    model_parameters: ModelParameters,
}

/// 学习算法类型
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LearningAlgorithm {
    /// 监督学习
    Supervised,
    /// 强化学习
    Reinforcement,
    /// 迁移学习
    Transfer,
    /// 联邦学习
    Federated,
}

/// 训练样本
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingExample {
    /// 输入特征
    input_features: HashMap<String, f64>,
    /// 期望输出
    expected_output: ConversionResult,
    /// 实际输出
    actual_output: ConversionResult,
    /// 质量评分
    quality_score: f64,
}

/// 模型参数
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelParameters {
    /// 学习率
    learning_rate: f64,
    /// 批处理大小
    batch_size: usize,
    /// 最大迭代次数
    max_iterations: u32,
    /// 收敛阈值
    convergence_threshold: f64,
}

/// 质量评估器
pub struct QualityAssessor {
    /// 评估指标
    evaluation_metrics: Vec<QualityMetric>,
    /// 权重配置
    weight_config: WeightConfiguration,
}

/// 质量指标
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum QualityMetric {
    /// 语义完整性
    SemanticCompleteness,
    /// 类型一致性
    TypeConsistency,
    /// 性能效率
    PerformanceEfficiency,
    /// 可扩展性
    Scalability,
    /// 互操作性
    Interoperability,
}

/// 权重配置
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WeightConfiguration {
    /// 语义完整性权重
    semantic_completeness_weight: f64,
    /// 类型一致性权重
    type_consistency_weight: f64,
    /// 性能效率权重
    performance_efficiency_weight: f64,
    /// 可扩展性权重
    scalability_weight: f64,
    /// 互操作性权重
    interoperability_weight: f64,
}

/// 转换规则
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TransformationRule {
    /// 规则ID
    rule_id: String,
    /// 规则名称
    rule_name: String,
    /// 规则描述
    description: String,
    /// 源模式
    source_pattern: String,
    /// 目标模式
    target_pattern: String,
    /// 转换函数
    transformation_function: TransformationFunction,
    /// 优先级
    priority: u32,
    /// 是否启用
    enabled: bool,
}

/// 转换函数
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TransformationFunction {
    /// 直接映射
    DirectMapping,
    /// 单位转换
    UnitConversion { from_unit: String, to_unit: String, conversion_factor: f64 },
    /// 数据类型转换
    DataTypeConversion { from_type: String, to_type: String },
    /// 自定义函数
    CustomFunction { function_name: String, parameters: HashMap<String, String> },
}

impl IndustrialSemanticConverter {
    /// 创建新的工业语义转换器
    pub fn new(config: ConverterConfiguration) -> Self {
        let rule_engine = RuleEngine::new();
        let semantic_cache = SemanticCache::new(config.cache_settings.clone());
        let learning_engine = LearningEngine::new();
        let quality_assessor = QualityAssessor::new();

        Self {
            config: RwLock::new(config),
            statistics: RwLock::new(ConversionStatistics::default()),
            rule_engine,
            semantic_cache,
            learning_engine,
            quality_assessor,
        }
    }

    /// 执行语义转换的核心逻辑
    async fn perform_semantic_conversion(
        &self,
        request: &ConversionRequest,
    ) -> AnyhowResult<ConversionResult> {
        let start_time = Instant::now();

        info!("开始语义转换: source_type={:?}, target_type={:?}",
              request.source_data_type, request.target_opcua_type);

        // 1. 数据预处理
        let preprocessed_data = self.preprocess_data(&request.source_schema).await?;

        // 2. 应用转换规则
        let transformed_data = self.apply_transformation_rules(
            &preprocessed_data,
            &request.transformation_rules,
        ).await?;

        // 3. 构建OPC UA模型
        let opcua_model = self.build_opcua_model(
            &transformed_data,
            &request.target_opcua_type,
        ).await?;

        // 4. 质量评估
        let quality_metrics = self.assess_conversion_quality(
            &request.source_schema,
            &opcua_model,
        ).await?;

        // 5. 计算转换统计
        let conversion_time = start_time.elapsed();
        let stats = ConversionStatistics {
            total_conversions: 1,
            successful_conversions: 1,
            failed_conversions: 0,
            avg_conversion_time_ms: conversion_time.as_millis() as f64,
            success_rate: 1.0,
            quality_score: self.calculate_overall_quality_score(&quality_metrics),
            performance_metrics: self.calculate_performance_metrics(conversion_time),
        };

        // 6. 缓存结果
        self.cache_conversion_result(&request.id, &opcua_model).await?;

        info!("语义转换完成: 耗时={:?}ms, 质量评分={:.2}",
              conversion_time.as_millis(), stats.quality_score);

        Ok(ConversionResult {
            id: request.id.clone(),
            source_schema: request.source_schema.clone(),
            opcua_model,
            conversion_rules: request.transformation_rules.clone(),
            quality_metrics,
            statistics: stats,
            conversion_time,
            timestamp: chrono::Utc::now(),
        })
    }

    /// 数据预处理
    async fn preprocess_data(&self, schema: &DataSourceSchema) -> AnyhowResult<DataSourceSchema> {
        debug!("开始数据预处理");

        // 实现数据清洗、标准化、验证等预处理逻辑
        let mut processed_schema = schema.clone();

        // 数据标准化
        self.standardize_data(&mut processed_schema).await?;

        // 数据验证
        self.validate_data(&processed_schema).await?;

        Ok(processed_schema)
    }

    /// 数据标准化
    async fn standardize_data(&self, schema: &mut DataSourceSchema) -> AnyhowResult<()> {
        // 实现数据标准化逻辑
        info!("执行数据标准化");
        Ok(())
    }

    /// 数据验证
    async fn validate_data(&self, schema: &DataSourceSchema) -> AnyhowResult<()> {
        // 实现数据验证逻辑
        info!("执行数据验证");
        Ok(())
    }

    /// 应用转换规则
    async fn apply_transformation_rules(
        &self,
        data: &DataSourceSchema,
        rules: &TransformationRules,
    ) -> AnyhowResult<TransformedData> {
        debug!("应用转换规则: 规则数量={}", rules.rules.len());

        let mut transformed_data = TransformedData::new();

        for rule in &rules.rules {
            if let Some(result) = self.rule_engine.apply_rule(rule, data).await? {
                transformed_data.merge(result);
            }
        }

        Ok(transformed_data)
    }

    /// 构建OPC UA模型
    async fn build_opcua_model(
        &self,
        data: &TransformedData,
        target_type: &str,
    ) -> AnyhowResult<OPCUAModel> {
        debug!("构建OPC UA模型: 目标类型={}", target_type);

        let mut model = OPCUAModel::new();

        // 设置命名空间
        model.namespaces = self.create_namespaces().await?;

        // 根据目标类型构建模型
        match target_type {
            "manufacturing_equipment" => self.build_manufacturing_model(data, &mut model).await?,
            "robotics_system" => self.build_robotics_model(data, &mut model).await?,
            "supply_chain" => self.build_supply_chain_model(data, &mut model).await?,
            "traffic_system" => self.build_traffic_model(data, &mut model).await?,
            _ => self.build_generic_model(data, &mut model).await?,
        }

        Ok(model)
    }

    /// 构建制造业设备模型
    async fn build_manufacturing_model(
        &self,
        data: &TransformedData,
        model: &mut OPCUAModel,
    ) -> AnyhowResult<()> {
        info!("构建制造业设备模型");

        // 创建设备对象类型
        let equipment_type = self.create_equipment_object_type().await?;
        model.add_object_type(equipment_type);

        // 创建设备实例
        for equipment_data in data.get_equipment_data() {
            let equipment_instance = self.create_equipment_instance(equipment_data).await?;
            model.add_node(equipment_instance);
        }

        Ok(())
    }

    /// 构建机器人系统模型
    async fn build_robotics_model(
        &self,
        data: &TransformedData,
        model: &mut OPCUAModel,
    ) -> AnyhowResult<()> {
        info!("构建机器人系统模型");

        // 创建机器人对象类型
        let robot_type = self.create_robot_object_type().await?;
        model.add_object_type(robot_type);

        // 创建机器人实例
        for robot_data in data.get_robot_data() {
            let robot_instance = self.create_robot_instance(robot_data).await?;
            model.add_node(robot_instance);
        }

        Ok(())
    }

    /// 构建供应链管理模型
    async fn build_supply_chain_model(
        &self,
        data: &TransformedData,
        model: &mut OPCUAModel,
    ) -> AnyhowResult<()> {
        info!("构建供应链管理模型");

        // 创建供应链对象类型
        let supply_chain_type = self.create_supply_chain_object_type().await?;
        model.add_object_type(supply_chain_type);

        // 创建供应链实例
        for sc_data in data.get_supply_chain_data() {
            let sc_instance = self.create_supply_chain_instance(sc_data).await?;
            model.add_node(sc_instance);
        }

        Ok(())
    }

    /// 构建交通工程模型
    async fn build_traffic_model(
        &self,
        data: &TransformedData,
        model: &mut OPCUAModel,
    ) -> AnyhowResult<()> {
        info!("构建交通工程模型");

        // 创建交通系统对象类型
        let traffic_type = self.create_traffic_object_type().await?;
        model.add_object_type(traffic_type);

        // 创建交通系统实例
        for traffic_data in data.get_traffic_data() {
            let traffic_instance = self.create_traffic_instance(traffic_data).await?;
            model.add_node(traffic_instance);
        }

        Ok(())
    }

    /// 构建通用模型
    async fn build_generic_model(
        &self,
        data: &TransformedData,
        model: &mut OPCUAModel,
    ) -> AnyhowResult<()> {
        info!("构建通用模型");

        // 创建通用对象类型
        let generic_type = self.create_generic_object_type().await?;
        model.add_object_type(generic_type);

        // 创建通用实例
        for generic_data in data.get_generic_data() {
            let generic_instance = self.create_generic_instance(generic_data).await?;
            model.add_node(generic_instance);
        }

        Ok(())
    }

    /// 创建命名空间
    async fn create_namespaces(&self) -> AnyhowResult<NamespaceArray> {
        let mut namespaces = NamespaceArray::new();
        namespaces.add("http://opcfoundation.org/UA/".to_string()); // 标准命名空间
        namespaces.add("http://huawei.com/industrial-iot/".to_string()); // 华为命名空间
        namespaces.add("http://industry-agent.com/semantic/".to_string()); // 语义转换器命名空间

        Ok(namespaces)
    }

    /// 评估转换质量
    async fn assess_conversion_quality(
        &self,
        source_schema: &DataSourceSchema,
        opcua_model: &OPCUAModel,
    ) -> AnyhowResult<ModelQualityMetrics> {
        debug!("开始质量评估");

        self.quality_assessor.assess(source_schema, opcua_model).await
    }

    /// 计算总体质量评分
    fn calculate_overall_quality_score(&self, metrics: &ModelQualityMetrics) -> f64 {
        // 实现质量评分计算逻辑
        let completeness_score = metrics.semantic_completeness * 0.3;
        let consistency_score = metrics.type_consistency * 0.25;
        let performance_score = metrics.performance_efficiency * 0.2;
        let scalability_score = metrics.scalability * 0.15;
        let interoperability_score = metrics.interoperability * 0.1;

        completeness_score + consistency_score + performance_score +
        scalability_score + interoperability_score
    }

    /// 计算性能指标
    fn calculate_performance_metrics(&self, conversion_time: Duration) -> crate::models::PerformanceMetrics {
        crate::models::PerformanceMetrics {
            conversion_time_ms: conversion_time.as_millis() as u64,
            memory_usage_mb: 0, // 需要实际测量
            cpu_usage_percent: 0.0, // 需要实际测量
            throughput_ops_per_sec: 1000.0 / conversion_time.as_secs_f64(),
        }
    }

    /// 缓存转换结果
    async fn cache_conversion_result(
        &self,
        request_id: &str,
        opcua_model: &OPCUAModel,
    ) -> AnyhowResult<()> {
        self.semantic_cache.cache_result(request_id, opcua_model).await
    }

    // 创建各种对象类型的辅助方法
    async fn create_equipment_object_type(&self) -> AnyhowResult<OPCUAObjectType> {
        // 实现设备对象类型创建
        todo!("实现设备对象类型创建")
    }

    async fn create_robot_object_type(&self) -> AnyhowResult<OPCUAObjectType> {
        // 实现机器人对象类型创建
        todo!("实现机器人对象类型创建")
    }

    async fn create_supply_chain_object_type(&self) -> AnyhowResult<OPCUAObjectType> {
        // 实现供应链对象类型创建
        todo!("实现供应链对象类型创建")
    }

    async fn create_traffic_object_type(&self) -> AnyhowResult<OPCUAObjectType> {
        // 实现交通对象类型创建
        todo!("实现交通对象类型创建")
    }

    async fn create_generic_object_type(&self) -> AnyhowResult<OPCUAObjectType> {
        // 实现通用对象类型创建
        todo!("实现通用对象类型创建")
    }

    async fn create_equipment_instance(&self, data: &EquipmentData) -> AnyhowResult<OPCUANode> {
        // 实现设备实例创建
        todo!("实现设备实例创建")
    }

    async fn create_robot_instance(&self, data: &RobotData) -> AnyhowResult<OPCUANode> {
        // 实现机器人实例创建
        todo!("实现机器人实例创建")
    }

    async fn create_supply_chain_instance(&self, data: &SupplyChainData) -> AnyhowResult<OPCUANode> {
        // 实现供应链实例创建
        todo!("实现供应链实例创建")
    }

    async fn create_traffic_instance(&self, data: &TrafficData) -> AnyhowResult<OPCUANode> {
        // 实现交通实例创建
        todo!("实现交通实例创建")
    }

    async fn create_generic_instance(&self, data: &GenericData) -> AnyhowResult<OPCUANode> {
        // 实现通用实例创建
        todo!("实现通用实例创建")
    }
}

// 实现异步特征
#[async_trait]
impl SemanticConverter for IndustrialSemanticConverter {
    async fn convert(&self, request: &ConversionRequest) -> Result<ConversionResult> {
        // 更新统计信息
        {
            let mut stats = self.statistics.write().await;
            stats.total_conversions += 1;
        }

        // 检查缓存
        if let Some(cached_result) = self.semantic_cache.get_cached_result(&request.id).await? {
            info!("使用缓存结果: request_id={}", request.id);
            return Ok(cached_result);
        }

        // 执行转换
        match self.perform_semantic_conversion(request).await {
            Ok(result) => {
                // 更新成功统计
                {
                    let mut stats = self.statistics.write().await;
                    stats.successful_conversions += 1;
                    stats.success_rate = stats.successful_conversions as f64 / stats.total_conversions as f64;
                }

                // 如果启用了学习模式，收集训练数据
                {
                    let config = self.config.read().await;
                    if matches!(config.conversion_mode, ConversionMode::Learning) {
                        self.learning_engine.collect_training_data(request, &result).await?;
                    }
                }

                Ok(result)
            },
            Err(e) => {
                // 更新失败统计
                {
                    let mut stats = self.statistics.write().await;
                    stats.failed_conversions += 1;
                    stats.success_rate = stats.successful_conversions as f64 / stats.total_conversions as f64;
                }

                error!("转换失败: request_id={}, error={}", request.id, e);
                Err(e.into())
            }
        }
    }

    async fn batch_convert(&self, requests: &[ConversionRequest]) -> Result<Vec<ConversionResult>> {
        info!("开始批量转换: 请求数量={}", requests.len());

        let config = self.config.read().await;
        let batch_size = config.performance_settings.batch_size;
        let parallelism = config.performance_settings.parallelism;

        let mut results = Vec::with_capacity(requests.len());

        // 分批并行处理
        for chunk in requests.chunks(batch_size.max(1)) {
            let mut tasks = Vec::new();

            for request in chunk {
                let converter = self.clone(); // 需要实现 Clone
                let request = request.clone();

                let task = tokio::spawn(async move {
                    converter.convert(&request).await
                });

                tasks.push(task);
            }

            // 等待当前批次完成
            for task in tasks {
                match task.await {
                    Ok(Ok(result)) => results.push(result),
                    Ok(Err(e)) => warn!("批量转换中的单个请求失败: {}", e),
                    Err(e) => error!("任务执行错误: {}", e),
                }
            }
        }

        info!("批量转换完成: 成功数量={}", results.len());
        Ok(results)
    }

    async fn validate_quality(&self, result: &ConversionResult) -> Result<QualityMetrics> {
        debug!("验证转换质量: result_id={}", result.id);

        let metrics = self.quality_assessor.detailed_assess(&result).await?;

        Ok(QualityMetrics {
            semantic_completeness: metrics.semantic_completeness,
            type_consistency: metrics.type_consistency,
            performance_efficiency: metrics.performance_efficiency,
            scalability: metrics.scalability,
            interoperability: metrics.interoperability,
            overall_score: self.calculate_overall_quality_score(&metrics),
        })
    }

    async fn optimize_rules(
        &self,
        rules: &TransformationRules,
        feedback: &QualityFeedback,
    ) -> Result<TransformationRules> {
        info!("开始优化转换规则");

        let optimized_rules = self.learning_engine.optimize_rules(rules, feedback).await?;

        Ok(optimized_rules)
    }

    async fn get_conversion_stats(&self) -> Result<ConversionStatistics> {
        let stats = self.statistics.read().await;
        Ok(stats.clone())
    }

    fn get_configuration(&self) -> ConverterConfiguration {
        // 注意：这里需要处理 RwLock，可能需要异步版本
        todo!("实现配置获取")
    }

    async fn update_configuration(&mut self, config: &ConverterConfiguration) -> Result<()> {
        *self.config.write().await = config.clone();
        info!("转换器配置已更新");
        Ok(())
    }
}

// 辅助数据结构
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TransformedData {
    equipment_data: Vec<EquipmentData>,
    robot_data: Vec<RobotData>,
    supply_chain_data: Vec<SupplyChainData>,
    traffic_data: Vec<TrafficData>,
    generic_data: Vec<GenericData>,
}

impl TransformedData {
    pub fn new() -> Self {
        Self {
            equipment_data: Vec::new(),
            robot_data: Vec::new(),
            supply_chain_data: Vec::new(),
            traffic_data: Vec::new(),
            generic_data: Vec::new(),
        }
    }

    pub fn merge(&mut self, other: TransformedData) {
        self.equipment_data.extend(other.equipment_data);
        self.robot_data.extend(other.robot_data);
        self.supply_chain_data.extend(other.supply_chain_data);
        self.traffic_data.extend(other.traffic_data);
        self.generic_data.extend(other.generic_data);
    }

    pub fn get_equipment_data(&self) -> &[EquipmentData] {
        &self.equipment_data
    }

    pub fn get_robot_data(&self) -> &[RobotData] {
        &self.robot_data
    }

    pub fn get_supply_chain_data(&self) -> &[SupplyChainData] {
        &self.supply_chain_data
    }

    pub fn get_traffic_data(&self) -> &[TrafficData] {
        &self.traffic_data
    }

    pub fn get_generic_data(&self) -> &[GenericData] {
        &self.generic_data
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EquipmentData {
    pub equipment_id: String,
    pub equipment_type: String,
    pub status: String,
    pub properties: HashMap<String, serde_json::Value>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RobotData {
    pub robot_id: String,
    pub robot_type: String,
    pub joint_count: u32,
    pub status: String,
    pub properties: HashMap<String, serde_json::Value>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SupplyChainData {
    pub item_id: String,
    pub item_type: String,
    pub location: String,
    pub status: String,
    pub properties: HashMap<String, serde_json::Value>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrafficData {
    pub device_id: String,
    pub device_type: String,
    pub location: String,
    pub status: String,
    pub properties: HashMap<String, serde_json::Value>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GenericData {
    pub entity_id: String,
    pub entity_type: String,
    pub category: String,
    pub properties: HashMap<String, serde_json::Value>,
}

// 其他组件的简化实现
impl RuleEngine {
    pub fn new() -> Self {
        Self {
            transformation_rules: HashMap::new(),
            semantic_mappings: SemanticMappings::new(),
            industry_rules: IndustrySpecificRules::new(),
        }
    }

    pub async fn apply_rule(
        &self,
        rule: &TransformationRule,
        data: &DataSourceSchema,
    ) -> AnyhowResult<Option<TransformedData>> {
        // 实现规则应用逻辑
        Ok(None)
    }
}

impl SemanticMappings {
    pub fn new() -> Self {
        Self {
            data_type_mappings: HashMap::new(),
            unit_mappings: HashMap::new(),
            semantic_tag_mappings: HashMap::new(),
            namespace_mappings: HashMap::new(),
        }
    }
}

impl IndustrySpecificRules {
    pub fn new() -> Self {
        Self {
            manufacturing: ManufacturingRules::new(),
            robotics: RoboticsRules::new(),
            supply_chain: SupplyChainRules::new(),
            traffic_engineering: TrafficEngineeringRules::new(),
        }
    }
}

impl ManufacturingRules {
    pub fn new() -> Self {
        Self {
            equipment_types: HashMap::new(),
            process_mappings: HashMap::new(),
            quality_standards: HashMap::new(),
        }
    }
}

impl RoboticsRules {
    pub fn new() -> Self {
        Self {
            joint_types: HashMap::new(),
            motion_types: HashMap::new(),
            control_modes: HashMap::new(),
        }
    }
}

impl SupplyChainRules {
    pub fn new() -> Self {
        Self {
            logistics_status: HashMap::new(),
            inventory_types: HashMap::new(),
            supplier_levels: HashMap::new(),
        }
    }
}

impl TrafficEngineeringRules {
    pub fn new() -> Self {
        Self {
            traffic_signals: HashMap::new(),
            vehicle_types: HashMap::new(),
            road_conditions: HashMap::new(),
        }
    }
}

impl SemanticCache {
    pub fn new(config: CacheSettings) -> Self {
        Self {
            cache_storage: RwLock::new(HashMap::new()),
            config,
        }
    }

    pub async fn get_cached_result(&self, request_id: &str) -> AnyhowResult<Option<ConversionResult>> {
        let cache = self.cache_storage.read().await;

        if let Some(cached) = cache.get(request_id) {
            // 检查缓存是否过期
            if cached.cached_at.elapsed() < Duration::from_secs(self.config.ttl_secs) {
                let mut result = cached.result.clone();
                result.timestamp = chrono::Utc::now(); // 更新时间戳
                return Ok(Some(result));
            }
        }

        Ok(None)
    }

    pub async fn cache_result(&self, request_id: &str, opcua_model: &OPCUAModel) -> AnyhowResult<()> {
        if !self.config.enabled {
            return Ok(());
        }

        let mut cache = self.cache_storage.write().await;

        let cached_result = CachedSemanticResult {
            result: ConversionResult {
                id: request_id.to_string(),
                source_schema: DataSourceSchema::default(), // 需要实际数据
                opcua_model: opcua_model.clone(),
                conversion_rules: TransformationRules::default(),
                quality_metrics: ModelQualityMetrics::default(),
                statistics: ConversionStatistics::default(),
                conversion_time: Duration::from_secs(0),
                timestamp: chrono::Utc::now(),
            },
            cached_at: Instant::now(),
            access_count: 0,
            quality_score: 0.0, // 需要实际计算
        };

        cache.insert(request_id.to_string(), cached_result);

        Ok(())
    }
}

impl LearningEngine {
    pub fn new() -> Self {
        Self {
            algorithm_type: LearningAlgorithm::Supervised,
            training_data: Vec::new(),
            model_parameters: ModelParameters::default(),
        }
    }

    pub async fn collect_training_data(
        &self,
        request: &ConversionRequest,
        result: &ConversionResult,
    ) -> AnyhowResult<()> {
        // 实现训练数据收集
        Ok(())
    }

    pub async fn optimize_rules(
        &self,
        rules: &TransformationRules,
        feedback: &QualityFeedback,
    ) -> AnyhowResult<TransformationRules> {
        // 实现规则优化
        Ok(rules.clone())
    }
}

impl LearningAlgorithm {
    // 实现学习算法
}

impl TrainingExample {
    // 实现训练样本相关方法
}

impl ModelParameters {
    pub fn default() -> Self {
        Self {
            learning_rate: 0.01,
            batch_size: 32,
            max_iterations: 1000,
            convergence_threshold: 1e-6,
        }
    }
}

impl QualityAssessor {
    pub fn new() -> Self {
        Self {
            evaluation_metrics: vec![
                QualityMetric::SemanticCompleteness,
                QualityMetric::TypeConsistency,
                QualityMetric::PerformanceEfficiency,
                QualityMetric::Scalability,
                QualityMetric::Interoperability,
            ],
            weight_config: WeightConfiguration::default(),
        }
    }

    pub async fn assess(
        &self,
        source_schema: &DataSourceSchema,
        opcua_model: &OPCUAModel,
    ) -> AnyhowResult<ModelQualityMetrics> {
        // 实现质量评估
        Ok(ModelQualityMetrics::default())
    }

    pub async fn detailed_assess(&self, result: &ConversionResult) -> AnyhowResult<ModelQualityMetrics> {
        // 实现详细质量评估
        Ok(result.quality_metrics.clone())
    }
}

impl WeightConfiguration {
    pub fn default() -> Self {
        Self {
            semantic_completeness_weight: 0.3,
            type_consistency_weight: 0.25,
            performance_efficiency_weight: 0.2,
            scalability_weight: 0.15,
            interoperability_weight: 0.1,
        }
    }
}

impl TransformationRule {
    // 实现转换规则相关方法
}

impl TransformationFunction {
    // 实现转换函数相关方法
}

impl Default for ConversionStatistics {
    fn default() -> Self {
        Self {
            total_conversions: 0,
            successful_conversions: 0,
            failed_conversions: 0,
            avg_conversion_time_ms: 0.0,
            success_rate: 0.0,
            quality_score: 0.0,
            performance_metrics: crate::models::PerformanceMetrics::default(),
        }
    }
}

impl Default for ModelQualityMetrics {
    fn default() -> Self {
        Self {
            semantic_completeness: 0.0,
            type_consistency: 0.0,
            performance_efficiency: 0.0,
            scalability: 0.0,
            interoperability: 0.0,
        }
    }
}

impl Default for crate::models::PerformanceMetrics {
    fn default() -> Self {
        Self {
            conversion_time_ms: 0,
            memory_usage_mb: 0,
            cpu_usage_percent: 0.0,
            throughput_ops_per_sec: 0.0,
        }
    }
}

impl Default for TransformationRules {
    fn default() -> Self {
        Self {
            rules: Vec::new(),
            metadata: HashMap::new(),
        }
    }
}

impl Default for DataSourceSchema {
    fn default() -> Self {
        Self {
            data_source_id: "default".to_string(),
            data_source_type: "generic".to_string(),
            schema_version: "1.0".to_string(),
            fields: Vec::new(),
            relationships: Vec::new(),
            constraints: Vec::new(),
            metadata: HashMap::new(),
        }
    }
}

impl Clone for IndustrialSemanticConverter {
    fn clone(&self) -> Self {
        // 注意：这里需要处理 RwLock 的克隆
        // 在实际实现中，可能需要使用 Arc<RwLock<>>
        unimplemented!("需要实现适当的克隆逻辑")
    }
}